Biology subjects

Raich, L.

Publications and source records attributed to Raich, L..

5 recordsLinked to original sources

bioRxiv2025

Amyloid-β 42-carrying extracellular vesicles are associated with neurodegeneration and neuroinflammation in Alzheimer's disease

Alzheimers disease (AD) is the most common form of dementia, characterized by progressive amyloid-{beta} (A{beta}) accumulation and neurodegeneration. Extracellular vesicles (EVs) have been implicated in AD pathology, but their relationship to disease progression and the cerebrospinal fluid (CSF) proteome remains poorly understood. In this study, we analysed plasma-derived A{beta}42-containing EVs from participants of the Alzheimers Disease Neuroimaging Initiative (ADNI) and calculated the ratio between the percentage of A{beta}42-positive peripheral EVs and CSF A{beta}42 (rA{beta}42). This ratio was elevated in AD and positively correlated with amyloid pathology as measured by PET. Longitudinal analyses revealed that higher rA{beta}42 values predicted accelerated hippocampal atrophy and cognitive decline, independent of CSF A{beta}42 concentrations. Proteomic profiling of CSF using the SomaScan platform showed that rA{beta}42 was associated with an inflammatory signature characterized by myeloid activation and type II interferon-related signalling pathways. Together, these findings indicate that A{beta}42-carrying EVs reflect an active and inflammatory component of AD pathology and may represent a novel class of biomarkers for disease progression. Understanding the mechanisms that link A{beta}42-positive EVs to neuroinflammatory signalling could open new avenues for pathway-specific diagnostics and therapeutic strategies in AD.

neuroscience↗

bioRxiv2024

Machine Learning Driven Simulations of SARS-CoV-2 Fitness Landscape

Predicting protein variant effects is a key challenge in preparing for pathogenic viral strains, understanding mutation-linked diseases, and designing new proteins. Protein sequence-structure-function relationships are difficult to model due to complex allosteric and epistatic effects. To investigate efficient modeling strategies, we trained supervised machine learning (ML) models with deep mutational scanning (DMS) libraries of SARS-CoV-2 receptor binding domain (RBD) sequences labeled with angiotensin converting enzyme 2 (ACE2) binding affinity. These models demonstrate superior performance predicting combinatorial mutation effects compared to adding or averaging the effects of point mutations and exhibit strong extrapolative performance ranking omicron variants when training only on wild type (WT) variants. We characterize the RBD fitness landscape combining ML with Markov Chain Monte Carlo simulations to predict evolutionary patterns from the WT sequence, and generate comparable sequence profiles to high fitness sequences in DMS data predicting mutations in unseen omicron variants. These models provide insight into the relationship between RBD sequence elements, and offer a new perspective on the use of DMS to predict emerging viral strains, which we anticipate will be applicable to other evolutionary prediction tasks. To facilitate application and future development of this strategy, we introduce Mavenets: https://github.com/SztainLab/mavenets.

bioinformatics↗

bioRxiv2020

Camostat mesylate inhibits SARS-CoV-2 activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity

Antiviral therapy is urgently needed to combat the coronavirus disease 2019 (COVID-19) pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The protease inhibitor camostat mesylate inhibits SARS-CoV-2 infection of lung cells by blocking the virus-activating host cell protease TMPRSS2. Camostat mesylate has been approved for treatment of pancreatitis in Japan and is currently being repurposed for COVID-19 treatment. However, potential mechanisms of viral resistance as well as camostat mesylate metabolization and antiviral activity of metabolites are unclear. Here, we show that SARS-CoV-2 can employ TMPRSS2-related host cell proteases for activation and that several of them are expressed in viral target cells. However, entry mediated by these proteases was blocked by camostat mesylate. The camostat metabolite GBPA inhibited the activity of recombinant TMPRSS2 with reduced efficiency as compared to camostat mesylate and was rapidly generated in the presence of serum. Importantly, the infection experiments in which camostat mesylate was identified as a SARS-CoV-2 inhibitor involved preincubation of target cells with camostat mesylate in the presence of serum for 2 h and thus allowed conversion of camostat mesylate into GBPA. Indeed, when the antiviral activities of GBPA and camostat mesylate were compared in this setting, no major differences were identified. Our results indicate that use of TMPRSS2-related proteases for entry into target cells will not render SARS-CoV-2 camostat mesylate resistant. Moreover, the present and previous findings suggest that the peak concentrations of GBPA established after the clinically approved camostat mesylate dose (600 mg/day) will result in antiviral activity.

molecular biology↗

bioRxiv2020

Molecular mechanism of SARS-CoV-2 cell entry inhibition via TMPRSS2 by Camostat and Nafamostat mesylate

The entry of the coronavirus SARS-CoV-2 into human cells can be inhibited by the approved drugs camostat and nafamostat. Here we elucidate the molecular mechanism of these drugs by combining experiments and simulations. In vitro assays confirm the hypothesis that both drugs act by inhibiting the human protein TMPRSS2. As no experimental structure is available, we provide a model of the TMPRSS2 equilibrium structure and its fluctuations by relaxing an initial homology structure with extensive 280 microseconds of all-atom molecular dynamics (MD) and Markov modeling. We describe the binding mode of both drugs with TMPRSS2 in a Michaelis complex (MC) state preceding the formation of a long-lived covalent inhibitory state. We find that nafamostat to has a higher MC population, which in turn leads to the more frequent formation of the covalent complex and thus higher inhibition efficacy, as confirmed in vitro and consistent with previous virus cell entry assays. Our TMPRSS2-drug structures are made public to guide the design of more potent and specific inhibitors.

biophysics↗

bioRxiv2020

Discovery of a hidden transient state in all bromodomain families

Bromodomains (BDs) are small protein modules that interact with acetylated marks in histones. These post-translational modifications are pivotal to regulate gene expression, making BDs promising targets to treat several diseases. While the general structure of BDs is well known, their dynamical features and their interplay with other macromolecules are poorly understood, hampering the rational design of potent and selective inhibitors. Here we combine extensive molecular dynamics simulations, Markov state modeling and structural data to reveal a novel and transiently formed state that is conserved across all BD families. It involves the breaking of two backbone hydrogen bonds that anchor the ZA-loop with the A helix, opening a cryptic pocket that partially occludes the one associated with histone binding. Our results suggest that this novel state is an allosteric regulatory switch for BDs, potentially related to a recently unveiled BD-DNA binding mode.

biophysics↗